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Calculate purity by comparing two cluster labeling variables

Source: R/clustering.R
calcPurity.Rd

This function aims at calculating the purity for the clustering result obtained with LIGER and the external clustering (existing "true" annotation). Purity can sometimes be a more useful metric when the clustering to be tested contains more subgroups or clusters than the true clusters. Purity ranges from 0 to 1, with a score of 1 representing a pure, accurate clustering.

The true clustering annotation must be specified as the base line. We suggest setting it to the object cellMeta so that it can be easily used for many other visualization and evaluation functions.

The purity can be calculated for only specified datasets, since true annotation might not be available for all datasets. Evaluation for only one or a few datasets can be done by specifying useDatasets. If useDatasets is specified, the argument checking for trueCluster and useCluster will be enforced to match the cells in the specified datasets.

Usage

calcPurity(
  object,
  trueCluster,
  useCluster = NULL,
  useDatasets = NULL,
  verbose = getOption("ligerVerbose", TRUE),
  classes.compare = trueCluster
)

Arguments

object

A liger object, with the clustering result present in cellMeta.

trueCluster

Either the name of one variable in cellMeta(object) or a factor object with annotation that matches with all cells being considered.

useCluster

The name of one variable in cellMeta(object). Default NULL uses default clusters.

useDatasets

A character vector of the names, a numeric or logical vector of the index of the datasets to be considered for the purity calculation. Default NULL uses all datasets.

verbose

Logical. Whether to show information of the progress. Default getOption("ligerVerbose") or TRUE if users have not set.

classes.compare

[Superseded] Use trueCluster instead.

Value

A numeric scalar, the purity of the clustering result indicated by useCluster compared to trueCluster.

Examples

# Assume the true cluster in `pbmcPlot` is "leiden_cluster"
# generate fake new labeling
fake <- sample(1:7, ncol(pbmcPlot), replace = TRUE)
# Insert into cellMeta
pbmcPlot$new <- factor(fake)
calcPurity(pbmcPlot, trueCluster = "leiden_cluster", useCluster = "new")
#> [1] 0.2466667

# Now assume we got existing base line annotation only for "stim" dataset
nStim <- ncol(dataset(pbmcPlot, "stim"))
stimTrueLabel <- factor(fake[1:nStim])
# Insert into cellMeta
cellMeta(pbmcPlot, "stim_true_label", useDatasets = "stim") <- stimTrueLabel
# Assume "leiden_cluster" is the clustering result we got and need to be
# evaluated
calcPurity(pbmcPlot, trueCluster = "stim_true_label",
           useCluster = "leiden_cluster", useDatasets = "stim")
#> [1] 0.115